In order to experimentally confirm how micro-gesture influences the classification accuracy of emotions, we feed the probability vectors yield by TSM [1]} into a RNN network. This is aiming to train an emotion understanding model via the results of clip-level MG recognition. More specifically, a vector (e.g., an output of the Softmax layer) that represents the probability distributions of a list of potential outcomes (possible MG classes), will be fed in a three-layered LSTM network (TSM [1]} with LSTM). After all vectors (clips) of a video are fed in, the labels of positive or negative (winning or losing of the match) can be used to train the network to understand the emotional states behind a series of micro-gestures in a holistic way. The reason why we select the TSM is because it obtains the best accuracy score in clip-level MG recognition. For comparison, the output vectors of proposed U-S-VAE are also utilized to train a similar emotion understanding network. In Table REF , we report the emotion understanding results of these models, according to the protocol (video-level) described in Sec. REF . We can observe and conclude that micro-gesture is helpful for the emotion understanding. TSM with LSTM (TSM + LSTM) can achieve the best score, U-S-VAE with a 35\(\%\)  MG recognition accuracy (U-S-VAE + LSTM) can beat most of the compared methods which further verifies that the MG-based analysis can benefit the final emotion understanding.
